International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 4, April 2014)
Superconducting Magnetic Energy Storage System based
Improvement of Power Quality on Wind/PV Systems
S. Ashwanth1, M. Manikandan2, Dr. A. Mahabub Basha3
1
PG scholar, Department of EEE, Erode Sengunthar Engineering College, Thudupathi, Erode, Tamilnadu, India.
2
Assistant Professor, Erode Sengunthar Engineering College, Thudupathi, Erode, Tamilnadu, India.
3
Professor and Director, K.S.R College of Engineering, Tiruchengode, Namakkal, Tamilnadu, India.
Combining renewable hybrid system with batteries as a
storage system, to increase duration of energy autonomy,
will make optimal use of the available renewable energy
resource and this in turn can guarantee high supply
reliability. To deal with different weather conditions and to
make the system supplies load demand at the worst
conditions, this strategy requires large storage capacity and
therefore it is very expensive. It is cheaper to supply peaks
or to supply demand during periods of cloudy weather or
poor wind days with another back up supply ( usually
diesel generator ), although this lowers the proportion of
renewable energy used. Selecting appropriate size of the
storage system is such that to minimize diesel running time
and to maximize fuel savings.Dump loads are
recommended to be used in hybrid power systems as
secondary loads to provide a sink for excess renewable
generated power to keep power balance of the system at all
times, also improve the economic return of the system by
allowing excess renewable energy to meet an on-site
energy needs that would otherwise have to be met with
other energy source.
Optimum match design is very important for PV/wind
hybrid system, which can guarantee battery bank working
at the optimum conditions as possible as can be, therefore
the battery bank’s lifetime can be prolonged to the
maximum and energy production cost decreased to the
minimum. In last few years, some commercial software
packages for simulating wind power, PV and hybrid
generating systems have been developed.
Abstract— In this research paper, Superconducting
Magnetic Energy Storage (SMES) is applied on wind energy
conversion systems (WECSs) that are equipped with Doubly
Fed Induction Generators (DFIGs) and Photo Voltaic (PV)
system during the presence of voltage sags and swells in the
grid side. The Wind and PV energy are suitable for hybrid
system because they are environmental friendly. Without
SMES, certain levels of voltage sags and swells in the grid side
may cause a critical operating condition that may require
disconnection of hybrid systems to the grid. Voltage sags are
an important power quality problem. The selection of a SMES
unit in this project is based on its advantages over other
energy storage technologies. Using the Hysteresis Current
Control approach in conjunction with a Fuzzy Logic
Controller, the SMES unit fruitfully and effectively enhances
the performance of the DFIG and PV systems during voltage
sag and swell events in the grid side. Thus, this will prevent
the hybrid systems from being disconnected from the grid.
The modeling of the wind energy conversion systems
(WECSs) that are equipped with doubly fed induction
generators (DFIGs) and Photo Voltic (PV) systems with
SMES is build using MATLAB/Simulink.
Keywords— Doubly Fed Induction Generators , Photo
Voltaic (PV), Power Quality, SMES, Wind.
I.
INTRODUCTION
A Hybrid Renewable Energy system is a system in
which two or more supplies from different renewable
energy sources (solar-thermal, solarphotovoltaic, wind,
biomass, hydropower, etc.) are integrated to supply
electricity or heat, or both, to the same demand. The most
frequently used hybrid system is the hybrid which consists
of Photovoltaic ( PV ) modules and wind turbines. Because
the supply pattern of different renewable energy sources
intermittent but with different patterns of intermittency, it is
often possible to achieve a better overall supply pattern by
integrating two or more sources. Sometimes also including
a form of energy storage. In this way the energy supply can
effectively be made less intermittent, or more firm.
II.
CONFIGURATION O F SMES
It was not until 1970s superconducting magnetic energy
storage (SMES) was primary proposed as a technology in
power systems. Energy is stored in the magnetic field
generated by circulating the DC current through a
superconducting coil. As can be seen from Fig.1[2], a SMES
system consists of several sub-systems.
676
International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 4, April 2014)
A large superconducting coil is the heart of a SMES
system, which is contained in a cryostat or dewar
consisting of a vacuum vessel and a liquid vessel that cools
the coil. A cryogenic system is also used to keep the
temperature well underneath the critical temperature of the
superconductor. An ac/dc PCS is used for two purposes:
One is to convert electrical energy from dc to ac, the other
is to charge and discharge the coil. Finally, a transformer
provides the connection to the power system reduces the
operating voltage to acceptable levels for the PCS.
E=
L
P=
= LI
Energy stored in a normal inductor will fade out rather
quickly due to the ohmic resistance in the coil when the
power supply is disconnected. Obviously this will not be
acceptable energy storage for use in a power system. The
ohmic resistance has to be removed before an inductor can
work for this purpose. This is possible by lowering the
temperature of the conductors, and by this making the
conductors superconducting. A superconducting wire is in
a state where the resistance in the material is zero. In this
state the current in a coil can flow for infinite time.
(1)
= VI
III.
METHODOLOGY
A. Modelling Of DFIG
Compared with a fixed-speed wind turbine generator,
DFIG is more complex because it requires more control
functions to control the rotor speed, pitch angle, and
terminal voltage, as well as GSC and RSC[1][13].
1. Model Structure of DFIG: The overall general model of
DFIG is shown in Fig. 2. It mainly consists of the models
for rotor, generator and converter, as well as controllers for
rotor speed, pitch angle and terminal voltage. In this
section, the wind speed model will not be discussed as it
can be simplified with the use of a constant or variable
value block according to the user preference. This block
can be a constant or signal source that is available in the
Simulink/MATLAB software package.
(2)
Fig.1 Components of SMES
For a SMES system, the inductively stored energy (E in
Joule) and the rated power (P in Watt) are commonly the
given specifications for SMES devices, and can be
expressed as before (1)&(2). Increasing any of these
parameters improves the energy/power capability of SMES.
But, there are other factors that need to be taken into
consideration which is as shown in below Table.1[3][4].
TABLE I
ENERGY CAPABILITY OF SMES
Fig. 2. General structure of the model for DFIG
Increasing Imax
Larger conductor cross section
2.Generator Model: The generator model is built up by the
well-known related equations that are provided for wound
rotor induction generator as below. The voltage equations
are based on the d-q (direct-quadrature) reference frame.
However, in this reference the equations are for squirrel
cage induction generator where udr and uqr are equal to
zero.For the simulation of WECS with the grid
disturbances, the converter model used in this thesis is
incorporated with a low frequency representation of the
behaviors of the converter during fault.
Larger current leads and associated
lead losses
Larger and more expensive PCS
Increasing Vmax
Larger more expensive PCS
Insulation problem
Increasing L
More turns in the magnet
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International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 4, April 2014)
3.Model of Rotor Speed Controller: The rotor speed
controller is aimed to achieve the optimal energy capture.
The relationship of power for the optimal energy capture
with the rotor speed is depicted in Fig. 3. At low wind
speed, the rotor speed is maintained at its minimum value
by adjusting the generator torque.
Fig. 6. I-V curves and P-V curves for a PV cell
IV.
SMES C ONTROL APPROACHES
Generally, there are two major configurations of SMES,
i.e., current source converter (CSC) and VSC.
Traditionally, CSC is connected through a 12-pulse
converter configuration to eliminate the ac-side fifth and
seventh harmonic currents and the dc side sixth harmonic
voltage, thus resulting in significant savings in harmonic
filters. However, because this configuration uses two 6pulse CSCs that are connected in parallel, its cost is
relatively high. The proposed SMES configuration used in
this paper consists of a VSC and dc–dc chopper. The
converter and the chopper are controlled using a Hysteresis
Current Controller (HCC) and a Fuzzy Logic Controller
(FLC) [2][6], respectively.
Fig. 3. Optimal rotor speed-power characteristic of typical variable
speed WECS
B. Modelling Of Pv System
The equivalent circuit of a PV cell is shown in Fig. 4.
A. Hysteresis Current Controller
The HCC is widely used because of its simplicity,
insensitivity to load parameter variations, fast dynamic
response,
and
inherent
maximum-current-limiting
characteristic. The basic implementation of the HCC is
based on deriving the switching signals from the
comparison of the actual phase current with a fixed
tolerance band around the reference current associated with
that phase. However, this type of band control is not only
depending on the corresponding phase voltage but is also
affected by the voltage of the other two phases. The effect
of interference between phases (referred to as interphase
dependence) can lead to high switching frequencies.
To maintain the advantages of the hysteresis methods,
this phase dependence can be minimized by using the
phase-locked loop (PLL) technique to maintain the
converter switching at a fixed predetermined frequency
level. The proposed SMES with an auxiliary PLL controller
is shown in Fig. 7. The HCC is comparing the three-phase
line currents (Iabc) with the reference currents (I* abc),
which is dictated by the I*d and I*q references. The values
of I*d and I*q are generated through conventional PI
controllers based on the error values of Vdc and Vs.
Fig. 4.PV cell equivalent circuit
It includes a current source, a diode, a series resistance
and a shunt resistance. As a result, the complete physical
behavior of the PV cell is in relation with Iph, Is , Rs and
Rsh from one hand and
with two environmental
parameters as the emperature and the solar radiation from
the other hand. The Matlab/SIMULINK model of Fig.5
was developed. For a given radiation, temperature, Rs and
Rsh, the I-V and P-V curves are generated as shown in
Fig.6.
Fig. 5. PV cell Matlab/SIMULINK model
678
International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 4, April 2014)
The output of the proposed controller is ΔIdref(t) which is
added to the previous state of current Idref(t-1) to obtain the
reference current Idref(t) . The others FLCs are based on the
same approach as in FLC1.The membership functions are
defined off- line, and the values of the variables are
selected according to the behavior of the variables observed
during simulations. The selected fuzzy sets for FLC1 are
shown in Fig. 8. The control rules of the FLC1 are
represented by a set of chosen fuzzy rules. The designed
fuzzy rules used in this work are given in Table 2. The
fuzzy sets have been defined as: NB, Negative Big, NS,
Negative Small, ZR, Zero, PS, Positive Small and PB,
Positive Big respectively.
The value of I*d and I* q is converted through Park
transformation (dq0 − abc) to produce the reference current
(I* abc).
Fig. 9. Membership function of input Error
Fig. 7. The proposed HCC control scheme
B. Fuzzy Logic Controller
For a good performance of DFIG based wind farm, four
fuzzy logic controllers FLC1, FLC2, FLC3, and FLC4 are
used. The PI controller in the dc bus voltage regulator is
replaced by the FLC1. The PI controller in the reactive
power regulator is replaced by the FLC2. The PI controllers
in current regulators of rotor side converter controller and
grid side converter controller are replaced by the FLC3 and
FLC4 respectively.
Fig. 10. Membership function of input Error Rate
Fig. 8. Fuzzy Logic Controller 1
In FLC1, the reference dc bus voltage Vdref is compared
with the actual voltage Vdc to obtain the voltage error
eVdc(t) as shown in Fig. 8. Also this error is compared with
the previous error eVdc(t-1) to get the change in error Δ
eVdc(t). The inputs of FLC1 are eVdc(t) and Δ eVdc(t).
Fig. 11. Membership function of fuzzy output
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Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 4, April 2014)
TABLE II
FUZZY RULES
V.
S IMULATION RESULTS & D ISCUSSION
A. Voltage Sag Event
Voltage sag is a decrease to between 0.1 and 0.9 pu in rm-s voltage or current at the power frequency for durations
of 0.5 cycles to 1.0 minute. Voltage sags are usually
associated with system faults but can also be caused by
switching of heavy loads or starting of large motors. A
voltage sag lasting for 0.2 s is applied at t=0.3 s at the grid
side of the system under study. Without the compensating
unit, the voltage sag can be occurred at the time between
0.3s to 0.5s. Then the magnitude of the voltage can be
reduced to zero for that particular fault time is shown in
Fig. 12. This will leads to misoperation of the loads or
sometimes tripping of the load circuits
Fig. 13. Voltage Sag with a compensating unit
With the inclusion of the compensating unit, the voltage
sag at the fault time of 0.3 to 0.5s shall cleared and the
magnitude of the voltage can be maintained on the same
level before and after the faults is shown in Fig. 13. Also as
compared to the conventional methods, the proposed
simulation shows the less variations during clearance of
faults at the time between 0.3s to 0.5s.
The amount of reactive power absorbed by the DFIG is
lesser with SMES connected to the PCC, since the voltage
profile at the PCC is rectified to a level below 1pu with the
connection of the compensating unit while this voltage will
remain above 1pu without compensating unit connected to
the PCC can be shown in Fig. 14.
Fig. 12. Voltage Sag without a compensating unit
Fig. 14. Real and Reactive power of the unit
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The Speed of the generator and nominal bus voltage
during which the fault can be cleared by means of
compensating unit is shown in the Fig.15. The nominal bus
voltage become smooth with the connection of
compensating unit in the wind generation systems.
Fig. 17. Voltage Swell without a compensating unit
In this simulation, a voltage swell is applied by
increasing the voltage level at the grid side to 1.35 pu. The
voltage swell is assumed to start at t= 0.3 s and lasts for
0.2s. In this event, DFIG generated power will increase
upon the swell occurrence and will be reduced when it is
cleared. Without the compensating unit, the magnitude of
the voltage can be increased beyond 1 pu for that particular
fault time is shown in Fig. 17. This will leads to failure of
fuse or sometimes sensitive loads too. With the inclusion of
the compensating unit, the voltage swell at the fault time of
0.3 to 0.5s shall cleared and the magnitude of the voltage
can be maintained on the same level before and after the
faults.
The following general observations can be concluded:
Fig. 15. Speed of the generator and nominal bus voltage
TABLE III
VARIOUS CONTROLLERS WITH TIME FOR MITIGATION OF
SAG
Fig. 16. PV panel output voltage
B. Voltage Swell Event
A swell is defined as an increase in r-m-s voltage or
current at the power frequency for durations from 0.5
cycles to 1.0 minute. Typical magnitudes are between 1.1
and 1.8 pu. As with dips, swells are usually associated with
system fault conditions, but they are much less common
than voltage dips. Swells can also be caused by switching
off a large load or switching on a large capacitor bank.
Types of controller
Without DVR
DVR
DVR with PI
SMES coil based DVR
681
Time for mitigation of
Sag
2ms
1.4ms
0.9ms
0.6ms
International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 4, April 2014)
[7]
From the Table III, time taken for mitigation of sag with
the SMES coil based DVR takes only 0.6ms, which is more
advantageous over all other controllers.
VI.
CONCLUSION
[8]
This paper has presented a Hysteresis Current Control in
conjunction with a Fuzzy Logic Controller to ensure the
dynamic response improvement of doubly fed induction
generator based wind farm and Photo Voltaic System under
remote fault condition. This new control algorithm along
with a new application of the SMES unit to improve the
transient response of WTGs equipped with DIFG and PV
systems during voltage sag and voltage swell events has
been proposed. Simulation results have shown that the
SMES unit is very effective in improving the dynamic
performance of a power system with hybrid systems during
voltage sag and voltage swell at the grid side. The proposed
control algorithm of the SMES unit is simple and easy to
implement. Also the results shows that the system has
greater reliability compare to system without SMES. Thus
the system with SMES is able to provide stable and less
variations in the output voltage to the connected loads.
[9]
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